"""Wrapper around xAI's Chat Completions API."""

from typing import (
    Any,
    Literal,
    Optional,
    TypeVar,
    Union,
)

import openai
from langchain_core.language_models.chat_models import (
    LangSmithParams,
    LanguageModelInput,
)
from langchain_core.messages import AIMessageChunk
from langchain_core.outputs import ChatGenerationChunk, ChatResult
from langchain_core.runnables import Runnable
from langchain_core.utils import secret_from_env
from langchain_openai.chat_models.base import BaseChatOpenAI
from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
from typing_extensions import Self

_BM = TypeVar("_BM", bound=BaseModel)
_DictOrPydanticClass = Union[dict[str, Any], type[_BM], type]
_DictOrPydantic = Union[dict, _BM]


class ChatXAI(BaseChatOpenAI):  # type: ignore[override]
    r"""ChatXAI chat model.

    Setup:
        Install ``langchain-xai`` and set environment variable ``XAI_API_KEY``.

        .. code-block:: bash

            pip install -U langchain-xai
            export XAI_API_KEY="your-api-key"


    Key init args — completion params:
        model: str
            Name of model to use.
        temperature: float
            Sampling temperature.
        max_tokens: Optional[int]
            Max number of tokens to generate.
        logprobs: Optional[bool]
            Whether to return logprobs.

    Key init args — client params:
        timeout: Union[float, Tuple[float, float], Any, None]
            Timeout for requests.
        max_retries: int
            Max number of retries.
        api_key: Optional[str]
            xAI API key. If not passed in will be read from env var `XAI_API_KEY`.

    Instantiate:
        .. code-block:: python

            from langchain_xai import ChatXAI

            llm = ChatXAI(
                model="grok-beta",
                temperature=0,
                max_tokens=None,
                timeout=None,
                max_retries=2,
                # api_key="...",
                # other params...
            )

    Invoke:
        .. code-block:: python

            messages = [
                (
                    "system",
                    "You are a helpful translator. Translate the user sentence to French.",
                ),
                ("human", "I love programming."),
            ]
            llm.invoke(messages)

        .. code-block:: python

            AIMessage(
                content="J'adore la programmation.",
                response_metadata={
                    'token_usage': {'completion_tokens': 9, 'prompt_tokens': 32, 'total_tokens': 41},
                    'model_name': 'grok-beta',
                    'system_fingerprint': None,
                    'finish_reason': 'stop',
                    'logprobs': None
                },
                id='run-168dceca-3b8b-4283-94e3-4c739dbc1525-0',
                usage_metadata={'input_tokens': 32, 'output_tokens': 9, 'total_tokens': 41})

    Stream:
        .. code-block:: python

            for chunk in llm.stream(messages):
                print(chunk.text(), end="")

        .. code-block:: python

            content='J' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
            content="'" id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
            content='ad' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
            content='ore' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
            content=' la' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
            content=' programm' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
            content='ation' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
            content='.' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
            content='' response_metadata={'finish_reason': 'stop', 'model_name': 'grok-beta'} id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'


    Async:
        .. code-block:: python

            await llm.ainvoke(messages)

            # stream:
            # async for chunk in (await llm.astream(messages))

            # batch:
            # await llm.abatch([messages])

        .. code-block:: python

            AIMessage(
                content="J'adore la programmation.",
                response_metadata={
                    'token_usage': {'completion_tokens': 9, 'prompt_tokens': 32, 'total_tokens': 41},
                    'model_name': 'grok-beta',
                    'system_fingerprint': None,
                    'finish_reason': 'stop',
                    'logprobs': None
                },
                id='run-09371a11-7f72-4c53-8e7c-9de5c238b34c-0',
                usage_metadata={'input_tokens': 32, 'output_tokens': 9, 'total_tokens': 41})

    Tool calling:
        .. code-block:: python

            from pydantic import BaseModel, Field

            llm = ChatXAI(model="grok-beta")

            class GetWeather(BaseModel):
                '''Get the current weather in a given location'''

                location: str = Field(
                    ..., description="The city and state, e.g. San Francisco, CA"
                )

            class GetPopulation(BaseModel):
                '''Get the current population in a given location'''

                location: str = Field(
                    ..., description="The city and state, e.g. San Francisco, CA"
                )

            llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
            ai_msg = llm_with_tools.invoke(
                "Which city is bigger: LA or NY?"
            )
            ai_msg.tool_calls


        .. code-block:: python

            [
                {
                    'name': 'GetPopulation',
                    'args': {'location': 'NY'},
                    'id': 'call_m5tstyn2004pre9bfuxvom8x',
                    'type': 'tool_call'
                },
                {
                    'name': 'GetPopulation',
                    'args': {'location': 'LA'},
                    'id': 'call_0vjgq455gq1av5sp9eb1pw6a',
                    'type': 'tool_call'
                }
            ]

    Structured output:
        .. code-block:: python

            from typing import Optional

            from pydantic import BaseModel, Field


            class Joke(BaseModel):
                '''Joke to tell user.'''

                setup: str = Field(description="The setup of the joke")
                punchline: str = Field(description="The punchline to the joke")
                rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")


            structured_llm = llm.with_structured_output(Joke)
            structured_llm.invoke("Tell me a joke about cats")

        .. code-block:: python

            Joke(
                setup='Why was the cat sitting on the computer?',
                punchline='To keep an eye on the mouse!',
                rating=7
            )

    Token usage:
        .. code-block:: python

            ai_msg = llm.invoke(messages)
            ai_msg.usage_metadata

        .. code-block:: python

            {'input_tokens': 37, 'output_tokens': 6, 'total_tokens': 43}

    Logprobs:
        .. code-block:: python

            logprobs_llm = llm.bind(logprobs=True)
            messages=[("human","Say Hello World! Do not return anything else.")]
            ai_msg = logprobs_llm.invoke(messages)
            ai_msg.response_metadata["logprobs"]

        .. code-block:: python

            {
                'content': None,
                'token_ids': [22557, 3304, 28808, 2],
                'tokens': [' Hello', ' World', '!', '</s>'],
                'token_logprobs': [-4.7683716e-06, -5.9604645e-07, 0, -0.057373047]
            }


    Response metadata
        .. code-block:: python

            ai_msg = llm.invoke(messages)
            ai_msg.response_metadata

        .. code-block:: python

            {
                'token_usage': {
                    'completion_tokens': 4,
                    'prompt_tokens': 19,
                    'total_tokens': 23
                    },
                'model_name': 'grok-beta',
                'system_fingerprint': None,
                'finish_reason': 'stop',
                'logprobs': None
            }

    """  # noqa: E501

    model_name: str = Field(alias="model")
    """Model name to use."""
    xai_api_key: Optional[SecretStr] = Field(
        alias="api_key",
        default_factory=secret_from_env("XAI_API_KEY", default=None),
    )
    """xAI API key.

    Automatically read from env variable `XAI_API_KEY` if not provided.
    """
    xai_api_base: str = Field(default="https://api.x.ai/v1/")
    """Base URL path for API requests."""
    search_parameters: Optional[dict[str, Any]] = None
    """Parameters for search requests. Example: ``{"mode": "auto"}``."""

    openai_api_key: Optional[SecretStr] = None
    openai_api_base: Optional[str] = None

    model_config = ConfigDict(
        populate_by_name=True,
    )

    @property
    def lc_secrets(self) -> dict[str, str]:
        """A map of constructor argument names to secret ids.

        For example,
            {"xai_api_key": "XAI_API_KEY"}
        """
        return {"xai_api_key": "XAI_API_KEY"}

    @classmethod
    def get_lc_namespace(cls) -> list[str]:
        """Get the namespace of the langchain object."""
        return ["langchain_xai", "chat_models"]

    @property
    def lc_attributes(self) -> dict[str, Any]:
        """List of attribute names that should be included in the serialized kwargs.

        These attributes must be accepted by the constructor.
        """
        attributes: dict[str, Any] = {}

        if self.xai_api_base:
            attributes["xai_api_base"] = self.xai_api_base

        return attributes

    @classmethod
    def is_lc_serializable(cls) -> bool:
        """Return whether this model can be serialized by Langchain."""
        return True

    @property
    def _llm_type(self) -> str:
        """Return type of chat model."""
        return "xai-chat"

    def _get_ls_params(
        self, stop: Optional[list[str]] = None, **kwargs: Any
    ) -> LangSmithParams:
        """Get the parameters used to invoke the model."""
        params = super()._get_ls_params(stop=stop, **kwargs)
        params["ls_provider"] = "xai"
        return params

    @model_validator(mode="after")
    def validate_environment(self) -> Self:
        """Validate that api key and python package exists in environment."""
        if self.n is not None and self.n < 1:
            raise ValueError("n must be at least 1.")
        if self.n is not None and self.n > 1 and self.streaming:
            raise ValueError("n must be 1 when streaming.")

        client_params: dict = {
            "api_key": (
                self.xai_api_key.get_secret_value() if self.xai_api_key else None
            ),
            "base_url": self.xai_api_base,
            "timeout": self.request_timeout,
            "default_headers": self.default_headers,
            "default_query": self.default_query,
        }
        if self.max_retries is not None:
            client_params["max_retries"] = self.max_retries

        if client_params["api_key"] is None:
            raise ValueError(
                "xAI API key is not set. Please set it in the `xai_api_key` field or "
                "in the `XAI_API_KEY` environment variable."
            )

        if not (self.client or None):
            sync_specific: dict = {"http_client": self.http_client}
            self.client = openai.OpenAI(
                **client_params, **sync_specific
            ).chat.completions
            self.root_client = openai.OpenAI(**client_params, **sync_specific)
        if not (self.async_client or None):
            async_specific: dict = {"http_client": self.http_async_client}
            self.async_client = openai.AsyncOpenAI(
                **client_params, **async_specific
            ).chat.completions
            self.root_async_client = openai.AsyncOpenAI(
                **client_params,
                **async_specific,
            )
        return self

    @property
    def _default_params(self) -> dict[str, Any]:
        """Get default parameters."""
        params = super()._default_params
        if self.search_parameters:
            if "extra_body" in params:
                params["extra_body"]["search_parameters"] = self.search_parameters
            else:
                params["extra_body"] = {"search_parameters": self.search_parameters}

        return params

    def _create_chat_result(
        self,
        response: Union[dict, openai.BaseModel],
        generation_info: Optional[dict] = None,
    ) -> ChatResult:
        rtn = super()._create_chat_result(response, generation_info)

        if not isinstance(response, openai.BaseModel):
            return rtn

        if hasattr(response.choices[0].message, "reasoning_content"):  # type: ignore
            rtn.generations[0].message.additional_kwargs["reasoning_content"] = (
                response.choices[0].message.reasoning_content  # type: ignore
            )

        if hasattr(response, "citations"):
            rtn.generations[0].message.additional_kwargs["citations"] = (
                response.citations
            )

        return rtn

    def _convert_chunk_to_generation_chunk(
        self,
        chunk: dict,
        default_chunk_class: type,
        base_generation_info: Optional[dict],
    ) -> Optional[ChatGenerationChunk]:
        generation_chunk = super()._convert_chunk_to_generation_chunk(
            chunk,
            default_chunk_class,
            base_generation_info,
        )
        if (choices := chunk.get("choices")) and generation_chunk:
            top = choices[0]
            if isinstance(generation_chunk.message, AIMessageChunk):
                if reasoning_content := top.get("delta", {}).get("reasoning_content"):
                    generation_chunk.message.additional_kwargs["reasoning_content"] = (
                        reasoning_content
                    )

        if (citations := chunk.get("citations")) and generation_chunk:
            if isinstance(generation_chunk.message, AIMessageChunk):
                generation_chunk.message.additional_kwargs["citations"] = citations

        return generation_chunk

    def with_structured_output(
        self,
        schema: Optional[_DictOrPydanticClass] = None,
        *,
        method: Literal[
            "function_calling", "json_mode", "json_schema"
        ] = "function_calling",
        include_raw: bool = False,
        strict: Optional[bool] = None,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, _DictOrPydantic]:
        """Model wrapper that returns outputs formatted to match the given schema.

        Args:
            schema:
                The output schema. Can be passed in as:

                - an OpenAI function/tool schema,
                - a JSON Schema,
                - a TypedDict class (support added in 0.1.20),
                - or a Pydantic class.

                If ``schema`` is a Pydantic class then the model output will be a
                Pydantic instance of that class, and the model-generated fields will be
                validated by the Pydantic class. Otherwise the model output will be a
                dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
                for more on how to properly specify types and descriptions of
                schema fields when specifying a Pydantic or TypedDict class.

            method: The method for steering model generation, one of:

                - "function_calling":
                    Uses xAI's `tool-calling features <https://docs.x.ai/docs/guides/function-calling>`_.
                - "json_schema":
                    Uses xAI's `structured output feature <https://docs.x.ai/docs/guides/structured-outputs>`_.
                - "json_mode":
                    Uses xAI's JSON mode feature.

            include_raw:
                If False then only the parsed structured output is returned. If
                an error occurs during model output parsing it will be raised. If True
                then both the raw model response (a BaseMessage) and the parsed model
                response will be returned. If an error occurs during output parsing it
                will be caught and returned as well. The final output is always a dict
                with keys "raw", "parsed", and "parsing_error".

            strict:

                - True:
                    Model output is guaranteed to exactly match the schema.
                    The input schema will also be validated according to
                    https://platform.openai.com/docs/guides/structured-outputs/supported-schemas
                - False:
                    Input schema will not be validated and model output will not be
                    validated.
                - None:
                    ``strict`` argument will not be passed to the model.

            kwargs: Additional keyword args aren't supported.

        Returns:
            A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.

            | If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs an instance of ``schema`` (i.e., a Pydantic object). Otherwise, if ``include_raw`` is False then Runnable outputs a dict.

            | If ``include_raw`` is True, then Runnable outputs a dict with keys:

            - "raw": BaseMessage
            - "parsed": None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
            - "parsing_error": Optional[BaseException]

        """  # noqa: E501
        # Some applications require that incompatible parameters (e.g., unsupported
        # methods) be handled.
        if method == "function_calling" and strict:
            strict = None
        return super().with_structured_output(
            schema, method=method, include_raw=include_raw, strict=strict, **kwargs
        )
